Detection and assessment of carotid arterial disease is important for the treatment of transient cerebral ischemic events and for the prevention of stroke. Arteriography, an expensive, invasive procedure, has a risk of morbidity and mortality which restricts its use to selected patients. Noninvasive, diagnostic ultrasound is being developed as an alternative to arteriography.
For carotid artery occlusive disease associated with blood flow abnormalities, as detected with Doppler ultrasound, irregular flow patterns proximal to the lesion area were studied by Keller (ref. 17), and were further quantified by Rutherford (ref. 41), who used hand-measured waveform parameters. Computer measured parameters obtained from the common carotid artery were used by Greene (ref. 39) and Sherriff (ref. 40).
To determine the extent of arterial disease from ultrasound Doppler shift data from the common and internal carotid arteries, the computer-assisted method of the present invention is used to recognize patterns in the spectral and temporal characteristics of the backscattered Doppler signal. This methodology, referred to as "statistical pattern recognition," has found application in disciplines ranging from chemical research (ref. 42) to the identification of liver abnormalities using diagnostic ultrasound (ref. 43). Such an approach is well suited to the problem of assessing vessel stenosis, where, while the precise nature of the physical processes involved are not well understood, the extent of disease can still be determined. The algorithms involved in the method of the present invention have produced diagnostically useful results with clinical data taken under realistic conditions. The processing involved extracts the Doppler shift data characteristic of the degree of stenosis from artifact created by vessel wall motion, sample volume movement, and system noise.
Traditionally, the diagnosis of aretery disease required invasive arteriography (contrast angiography). More recently, ultrasound scanning has led to the diagnosis of disease based upon displays of the Fourier Transform spectra as analyzed by a spectrum analyzer. Such reading is difficult. The recorded spectra (as shown, for example) in the Knox et al., article (Ref. 25)) are a continual broadening of the signal as the degree of stenosis increases. To read the spectra to quantify the degree of stenosis requires great skill and experience. To detect a slowly changing condition is almost impossible.
With the methodology of the present invention, however, the amplitude/frequency data of the discrete fast Fourier Transform is processed in the computer using pattern recognition algorithms. The method processes the data of the charted spectra by extracting useful pieces or predominant characteristics of the data. With the present system, the diagnosis is obtained objectively and automatically from the material data, thereby eliminating the need of great skill or experience in interpretation. A technician can arrive at a diagnosis immediately without the need for a radiologist to read the chart, with the consequent delay in the diagnosis. Visual interpretation of such features as the peak systolic frequency, the diastolic frequency, the amount of spectral broadening, or the overall shape of the waveforms makes it virtually impossible for even the most skilled radiologists to classify various stenoses objectively and to quantify which features are associated with each degree of stenosis. Pattern recognition of amplitude and frequency data offers a sophisticated statistical and analytic methodology that dissects the ultrasound Doppler spectra to extract the important diagnostic information automatically.